February 2026

arXiv:2602.12486v1 Announce Type: new Abstract: Humans appear to represent objects for intuitive physics with coarse, volumetric bodies” that smooth concavities – trading fine visual details for efficient physical predictions – yet their internal structure is largely unknown. Segmentation models, in contrast, optimize pixel-accurate masks that may misalign with such bodies. We ask whether and when these models nonetheless acquire human-like bodies. Using a time-to-collision (TTC) behavioral paradigm, we introduce a comparison pipeline and alignment metric, then vary model […]

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arXiv:2602.12485v1 Announce Type: new Abstract: The success of businesses depends on their ability to convert consumers into loyal customers. A customer’s value proposition is a primary determinant in this process, requiring a balance between affordability and long-term brand equity. Broad marketing campaigns can erode perceived brand value and reduce return on investment, while existing economic algorithms often misidentify highly engaged customers as ideal targets, leading to inefficient engagement and conversion outcomes. This work introduces a two-stage multi-model architecture […]

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arXiv:2602.12484v1 Announce Type: new Abstract: Grapes are among the most economically and culturally significant fruits on a global scale, and table grapes and wine are produced in significant quantities in Europe and Asia. The production and quality of grapes are significantly impacted by grape diseases such as Bacterial Rot, Downy Mildew, and Powdery Mildew. Consequently, the sustainable management of a vineyard necessitates the early and precise identification of these diseases. Current automated methods, particularly those that are based […]

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arXiv:2602.12483v1 Announce Type: new Abstract: Randomized Kaczmarz (RK) is a simple and fast solver for consistent overdetermined systems, but it is known to be fragile under noise. We study overdetermined $mtimes n$ linear systems with a sparse set of corrupted equations, $ {bf A}{bf x}^star = {bf b}, $where only $tilde{bf b} = {bf b} + boldsymbol{varepsilon}$ is observed with $|boldsymbol{varepsilon}|_0 le beta m$. The recently introduced QuantileRK (QRK) algorithm addresses this issue by testing residuals against a […]

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arXiv:2602.12482v1 Announce Type: new Abstract: We give a geometric construction of neural networks that separate disjoint compact subsets of $Bbb R^n$, and use it to obtain a constructive universal approximation theorem. Specifically, we show that networks with two hidden layers and either a sigmoidal activation (i.e., strictly monotone bounded continuous) or the ReLU activation can approximate any real-valued continuous function on an arbitrary compact set $KsubsetBbb R^n$ to any prescribed accuracy in the uniform norm. For finite $K$, […]

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arXiv:2602.12481v1 Announce Type: new Abstract: Online advertising platforms must decide how to allocate multiple ads across limited screen real estate, where each ad’s effectiveness depends not only on its own placement but also on nearby ads competing for user attention. Such spatial externalities – arising from proximity, clutter, or crowding – can significantly alter welfare and revenue outcomes, yet existing auction and allocation models typically treat ad slots as independent or ordered along a single dimension. We introduce […]

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arXiv:2602.12480v1 Announce Type: new Abstract: The proliferation of Transformer models is often constrained by the significant computational and memory bandwidth demands of deployment. To address this, we present MXFormer, a novel, hybrid, weight-stationary Compute-in-Memory (CIM) accelerator that provides high throughput and efficiency for fixed-model inference on large short-sequence Transformers. Our architecture’s foundation is the use of ultra-dense Charge-Trap Transistors (CTTs) in Microscaling MXFP4 CIM arrays, uniquely enabling the on-chip storage of up to hundreds of millions of parameters […]

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arXiv:2602.12476v1 Announce Type: new Abstract: Artificial intelligence (AI) companions are increasingly promoted as solutions for loneliness, often overlooking how personal dispositions and life-stage conditions shape artificial intimacy. Because intimacy is a primary coping mechanism for loneliness that varies by attachment style and age, we examine how different types of users form intimate relationships with AI companions in response to loneliness. Drawing on a hermeneutic literature review and a survey of 277 active AI companion users, we develop and […]

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arXiv:2602.12473v1 Announce Type: new Abstract: The paper develops a methodology, Grid-ECO, to optimally allocate electric vehicle charging stations (EVCS) within a distribution feeder, while considering EV charging demand at census-level granularity. The underlying problem is NP-hard and requires satisfying nonlinear, nonconvex, three-phase unbalanced AC network constraints while including integer decision variables. Existing works cannot guarantee AC feasibility nor optimality of this problem without either i) relaxing the integer decision variable space or ii) convexifying AC constraints. Proposed Grid-ECO […]

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arXiv:2602.12471v1 Announce Type: new Abstract: We consider the optimization problem of minimizing the logistic loss with gradient descent to train a linear model for binary classification with separable data. With a budget of $T$ iterations, it was recently shown that an accelerated $1/T^2$ rate is possible by choosing a large step size $eta = Theta(gamma^2 T)$ (where $gamma$ is the dataset’s margin) despite the resulting non-monotonicity of the loss. In this paper, we provide a tighter analysis of […]

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